Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process

Autores
Gamboa, Juliana; Campañone, Laura Analia
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In the present work, supervised machine learning (ML) algorithms, k-NN and SVM, were applied to classify strawberry samples during a microwave-assisted drying process. A dataset of 1150 strawberry records containinginformation about images of two pre-treatments types (fresh, FR and osmotically pre-treated, OD), three ranges of drying times (short <40 min; intermediate: 40-70 min and long> 70 min) and three physical characteristics previously selected (shrinkage, brightness and saturation) was used to perform the ML classifiers. The k-NN and SVM models led to good accuracy values, 0.94 for sample type and 0.90 for drying time categories. Since colour and morphological changes are related to changes in the product quality, these results are useful to evaluate the losses of nutritional and sensorialproperties taking place during the in-line processing of strawberries, by means of a non-invasive monitoring technique.
Fil: Gamboa, Juliana. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; Argentina
Fil: Campañone, Laura Analia. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; Argentina
VIII Congreso de Matemática Aplicada, Computacional e Industrial
Santa Fé
Argentina
Asociación Argentina de Matemática Aplicada, Computacional e Industrial
Universidad Nacional de La Plata
Materia
MACHINE LEARNING
NON-INVASIVE FOOD QUALITY MONITORING
CLASSIFICATION ALGORITHMS
MICROWAVES ASSISTED DRYING
STRAWBERRY
DIGITAL IMAGE ANALYSIS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/200986
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying processGamboa, JulianaCampañone, Laura AnaliaMACHINE LEARNINGNON-INVASIVE FOOD QUALITY MONITORINGCLASSIFICATION ALGORITHMSMICROWAVES ASSISTED DRYINGSTRAWBERRYDIGITAL IMAGE ANALYSIShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In the present work, supervised machine learning (ML) algorithms, k-NN and SVM, were applied to classify strawberry samples during a microwave-assisted drying process. A dataset of 1150 strawberry records containinginformation about images of two pre-treatments types (fresh, FR and osmotically pre-treated, OD), three ranges of drying times (short <40 min; intermediate: 40-70 min and long> 70 min) and three physical characteristics previously selected (shrinkage, brightness and saturation) was used to perform the ML classifiers. The k-NN and SVM models led to good accuracy values, 0.94 for sample type and 0.90 for drying time categories. Since colour and morphological changes are related to changes in the product quality, these results are useful to evaluate the losses of nutritional and sensorialproperties taking place during the in-line processing of strawberries, by means of a non-invasive monitoring technique.Fil: Gamboa, Juliana. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; ArgentinaFil: Campañone, Laura Analia. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; ArgentinaVIII Congreso de Matemática Aplicada, Computacional e IndustrialSanta FéArgentinaAsociación Argentina de Matemática Aplicada, Computacional e IndustrialUniversidad Nacional de La PlataAsociación Argentina de Matemática Aplicada, Computacional e Industrial2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/200986Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process; VIII Congreso de Matemática Aplicada, Computacional e Industrial; Santa Fé; Argentina; 2021; 407-410CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://asamaci.org.ar/revista-maci/info:eu-repo/semantics/altIdentifier/url/https://asamaci.org.ar/maci2021/Nacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:39:45Zoai:ri.conicet.gov.ar:11336/200986instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:39:45.904CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process
title Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process
spellingShingle Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process
Gamboa, Juliana
MACHINE LEARNING
NON-INVASIVE FOOD QUALITY MONITORING
CLASSIFICATION ALGORITHMS
MICROWAVES ASSISTED DRYING
STRAWBERRY
DIGITAL IMAGE ANALYSIS
title_short Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process
title_full Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process
title_fullStr Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process
title_full_unstemmed Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process
title_sort Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process
dc.creator.none.fl_str_mv Gamboa, Juliana
Campañone, Laura Analia
author Gamboa, Juliana
author_facet Gamboa, Juliana
Campañone, Laura Analia
author_role author
author2 Campañone, Laura Analia
author2_role author
dc.subject.none.fl_str_mv MACHINE LEARNING
NON-INVASIVE FOOD QUALITY MONITORING
CLASSIFICATION ALGORITHMS
MICROWAVES ASSISTED DRYING
STRAWBERRY
DIGITAL IMAGE ANALYSIS
topic MACHINE LEARNING
NON-INVASIVE FOOD QUALITY MONITORING
CLASSIFICATION ALGORITHMS
MICROWAVES ASSISTED DRYING
STRAWBERRY
DIGITAL IMAGE ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In the present work, supervised machine learning (ML) algorithms, k-NN and SVM, were applied to classify strawberry samples during a microwave-assisted drying process. A dataset of 1150 strawberry records containinginformation about images of two pre-treatments types (fresh, FR and osmotically pre-treated, OD), three ranges of drying times (short <40 min; intermediate: 40-70 min and long> 70 min) and three physical characteristics previously selected (shrinkage, brightness and saturation) was used to perform the ML classifiers. The k-NN and SVM models led to good accuracy values, 0.94 for sample type and 0.90 for drying time categories. Since colour and morphological changes are related to changes in the product quality, these results are useful to evaluate the losses of nutritional and sensorialproperties taking place during the in-line processing of strawberries, by means of a non-invasive monitoring technique.
Fil: Gamboa, Juliana. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; Argentina
Fil: Campañone, Laura Analia. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Criotecnología de Alimentos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Criotecnología de Alimentos; Argentina
VIII Congreso de Matemática Aplicada, Computacional e Industrial
Santa Fé
Argentina
Asociación Argentina de Matemática Aplicada, Computacional e Industrial
Universidad Nacional de La Plata
description In the present work, supervised machine learning (ML) algorithms, k-NN and SVM, were applied to classify strawberry samples during a microwave-assisted drying process. A dataset of 1150 strawberry records containinginformation about images of two pre-treatments types (fresh, FR and osmotically pre-treated, OD), three ranges of drying times (short <40 min; intermediate: 40-70 min and long> 70 min) and three physical characteristics previously selected (shrinkage, brightness and saturation) was used to perform the ML classifiers. The k-NN and SVM models led to good accuracy values, 0.94 for sample type and 0.90 for drying time categories. Since colour and morphological changes are related to changes in the product quality, these results are useful to evaluate the losses of nutritional and sensorialproperties taking place during the in-line processing of strawberries, by means of a non-invasive monitoring technique.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Congreso
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/200986
Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process; VIII Congreso de Matemática Aplicada, Computacional e Industrial; Santa Fé; Argentina; 2021; 407-410
CONICET Digital
CONICET
url http://hdl.handle.net/11336/200986
identifier_str_mv Evaluation of machine learning algorithms -k nearest neighbors and support vector machines- for strawberries classification during food drying process; VIII Congreso de Matemática Aplicada, Computacional e Industrial; Santa Fé; Argentina; 2021; 407-410
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/url/https://asamaci.org.ar/maci2021/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.coverage.none.fl_str_mv Nacional
dc.publisher.none.fl_str_mv Asociación Argentina de Matemática Aplicada, Computacional e Industrial
publisher.none.fl_str_mv Asociación Argentina de Matemática Aplicada, Computacional e Industrial
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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